Interspike interval correlations in neuron models with adaptation and correlated noise.

The generation of neural action potentials (spikes) is random but nevertheless may result in a rich statistical structure of the spike sequence. In particular, contrary to the popular renewal assumption of theoreticians, the intervals between adjacent spikes are often correlated. Experimentally, dif...

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Autores principales:	Lukas Ramlow, Benjamin Lindner
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/6c0369dbeae74307a60750df2087c863
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spelling	oai:doaj.org-article:6c0369dbeae74307a60750df2087c8632021-12-02T19:58:00ZInterspike interval correlations in neuron models with adaptation and correlated noise.1553-734X1553-735810.1371/journal.pcbi.1009261https://doaj.org/article/6c0369dbeae74307a60750df2087c8632021-08-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009261https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358The generation of neural action potentials (spikes) is random but nevertheless may result in a rich statistical structure of the spike sequence. In particular, contrary to the popular renewal assumption of theoreticians, the intervals between adjacent spikes are often correlated. Experimentally, different patterns of interspike-interval correlations have been observed and computational studies have identified spike-frequency adaptation and correlated noise as the two main mechanisms that can lead to such correlations. Analytical studies have focused on the single cases of either correlated (colored) noise or adaptation currents in combination with uncorrelated (white) noise. For low-pass filtered noise or adaptation, the serial correlation coefficient can be approximated as a single geometric sequence of the lag between the intervals, providing an explanation for some of the experimentally observed patterns. Here we address the problem of interval correlations for a widely used class of models, multidimensional integrate-and-fire neurons subject to a combination of colored and white noise sources and a spike-triggered adaptation current. Assuming weak noise, we derive a simple formula for the serial correlation coefficient, a sum of two geometric sequences, which accounts for a large class of correlation patterns. The theory is confirmed by means of numerical simulations in a number of special cases including the leaky, quadratic, and generalized integrate-and-fire models with colored noise and spike-frequency adaptation. Furthermore we study the case in which the adaptation current and the colored noise share the same time scale, corresponding to a slow stochastic population of adaptation channels; we demonstrate that our theory can account for a nonmonotonic dependence of the correlation coefficient on the channel's time scale. Another application of the theory is a neuron driven by network-noise-like fluctuations (green noise). We also discuss the range of validity of our weak-noise theory and show that by changing the relative strength of white and colored noise sources, we can change the sign of the correlation coefficient. Finally, we apply our theory to a conductance-based model which demonstrates its broad applicability.Lukas RamlowBenjamin LindnerPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 8, p e1009261 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Lukas Ramlow Benjamin Lindner Interspike interval correlations in neuron models with adaptation and correlated noise.
description	The generation of neural action potentials (spikes) is random but nevertheless may result in a rich statistical structure of the spike sequence. In particular, contrary to the popular renewal assumption of theoreticians, the intervals between adjacent spikes are often correlated. Experimentally, different patterns of interspike-interval correlations have been observed and computational studies have identified spike-frequency adaptation and correlated noise as the two main mechanisms that can lead to such correlations. Analytical studies have focused on the single cases of either correlated (colored) noise or adaptation currents in combination with uncorrelated (white) noise. For low-pass filtered noise or adaptation, the serial correlation coefficient can be approximated as a single geometric sequence of the lag between the intervals, providing an explanation for some of the experimentally observed patterns. Here we address the problem of interval correlations for a widely used class of models, multidimensional integrate-and-fire neurons subject to a combination of colored and white noise sources and a spike-triggered adaptation current. Assuming weak noise, we derive a simple formula for the serial correlation coefficient, a sum of two geometric sequences, which accounts for a large class of correlation patterns. The theory is confirmed by means of numerical simulations in a number of special cases including the leaky, quadratic, and generalized integrate-and-fire models with colored noise and spike-frequency adaptation. Furthermore we study the case in which the adaptation current and the colored noise share the same time scale, corresponding to a slow stochastic population of adaptation channels; we demonstrate that our theory can account for a nonmonotonic dependence of the correlation coefficient on the channel's time scale. Another application of the theory is a neuron driven by network-noise-like fluctuations (green noise). We also discuss the range of validity of our weak-noise theory and show that by changing the relative strength of white and colored noise sources, we can change the sign of the correlation coefficient. Finally, we apply our theory to a conductance-based model which demonstrates its broad applicability.
format	article
author	Lukas Ramlow Benjamin Lindner
author_facet	Lukas Ramlow Benjamin Lindner
author_sort	Lukas Ramlow
title	Interspike interval correlations in neuron models with adaptation and correlated noise.
title_short	Interspike interval correlations in neuron models with adaptation and correlated noise.
title_full	Interspike interval correlations in neuron models with adaptation and correlated noise.
title_fullStr	Interspike interval correlations in neuron models with adaptation and correlated noise.
title_full_unstemmed	Interspike interval correlations in neuron models with adaptation and correlated noise.
title_sort	interspike interval correlations in neuron models with adaptation and correlated noise.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/6c0369dbeae74307a60750df2087c863
work_keys_str_mv	AT lukasramlow interspikeintervalcorrelationsinneuronmodelswithadaptationandcorrelatednoise AT benjaminlindner interspikeintervalcorrelationsinneuronmodelswithadaptationandcorrelatednoise
_version_	1718375781584863232

Interspike interval correlations in neuron models with adaptation and correlated noise.

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